Model Predictive Control (MPC) is the most widely used advanced process control technology applied in process industries. There are more than 10,000 worldwide applications currently in service. A MPC controller relies on a model to predict the process behavior (the controlled variables, CV) and makes changes to the manipulated variables (MV) so that it can keep the process running inside a prescribed constraint set. When inside the constraint set, a MPC controller can also make changes to MVs so that the process is optimized based on a given economic objective function. A MPC controller is a real-time application which runs from cycle to cycle with a fixed time interval (the typical cycle time is one minute) so that it can keep up with the process dynamics.
In a real-time industrial MPC application, there always exist uncertainties or errors in the model. The feedback correction in the controller can overcome the control errors caused by the model uncertainty; but significant model uncertainty may affect controller performance adversely. Further, strong interactions among multiple MVs and CVs in the model can affect the controller behavior in a significant way if some of them have a nearly collinear relationship. The control performance issues caused by model error and near collinearity may include oscillation of key variables, frequent violation of CV constraints, and the movement of MVs to undesirable operating points.